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Recursive models of dynamic linear economies / Lars Peter Hansen, Thomas J. Sargent.

Por: Colaborador(es): Tipo de material: TextoTextoIdioma: Inglés Detalles de publicación: Princeton, New Jersey : Princeton University Press, 2014.Descripción: xv, 399 páginas : ilustraciones, gráficas a blanco y negro ; 26 cmISBN:
  • 9780691042770
Tema(s): Clasificación LoC:
  • HB 135  .H357 2014
Contenidos incompletos:
Preface, xiii -- Acknowledgments, xv -- Part I. Overview: theory and econometrics, 3 -- Part II. Tools -- 2. Linear stochastic difference equations, 15 -- 3. Efficient computations, 33 -- Part III. Components of economies -- 4. Economic environments, 61 -- 5. Optimal resource allocations, 79 -- 6. A commodity space, 125 -- 7. Competitive economies, 131 -- Part IV. Representations and properties -- 8. Statical representations, 153 -- 9. Canonical household technologies, 191 -- 10. Examples, 217 -- 11. Permanent income models, 233 -- 12. Gorman heterogeneous households, 253 -- 13. Complete markets aggregation, 269 -- 14. Periodic models of seasonality, 291 -- A. Matlab Programs, 327 -- References, 379 -- Subject index, 393 -- Author index, 397 -- Matlab index, 399
Resumen: This book views many apparently disparate dynamic economic models as examples of a single class of models that can be adapted and specialized to study diverse economic phenomena. The class of models was created by using recent advances in (i) the theory of recursive dynamic competitive economies; 1 (ii) methods for estimating and interpreting vector autoregression; 2 (iii) linear optimal control theory;3 and (iv) computer languages for rapidly manipulating linear optimal control systems.4 We combine these elements to build a class of models for which the competitive equilibria are vector autoregressions that can be swiftly computed, represented, and simulated using the methods of linear optimal control theory. We use the computer language Matlab to implement the computations. This language has a powerful vocabulary and a convenient structure that liberate time and energy from programming, and thereby spur creative application of linear control theory. Our goal has been to create a class of models that merge recursive economic theory and with dynamic econometrics. Systems of autoregressions and of mixed autogregressive, moving average processes are a dominant setting for dynamic econometrics. We constructed our economic models by adopting a version of recursive competitive theory in which an outcome of theorizing is a vector autoregression.
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Monografía - Colección General SUCURSAL JUAN PABLO DUARTE Estantería HB 135 .H357 2014 (Navegar estantería(Abre debajo)) Disponible 1049638

Preface, xiii -- Acknowledgments, xv -- Part I. Overview: theory and econometrics, 3 -- Part II. Tools -- 2. Linear stochastic difference equations, 15 -- 3. Efficient computations, 33 -- Part III. Components of economies -- 4. Economic environments, 61 -- 5. Optimal resource allocations, 79 -- 6. A commodity space, 125 -- 7. Competitive economies, 131 -- Part IV. Representations and properties -- 8. Statical representations, 153 -- 9. Canonical household technologies, 191 -- 10. Examples, 217 -- 11. Permanent income models, 233 -- 12. Gorman heterogeneous households, 253 -- 13. Complete markets aggregation, 269 -- 14. Periodic models of seasonality, 291 -- A. Matlab Programs, 327 -- References, 379 -- Subject index, 393 -- Author index, 397 -- Matlab index, 399

This book views many apparently disparate dynamic economic models as examples of a single class of models that can be adapted and specialized to study diverse economic phenomena. The class of models was created by using recent advances in (i) the theory of recursive dynamic competitive economies; 1 (ii) methods for estimating and interpreting vector autoregression; 2 (iii) linear optimal control theory;3 and (iv) computer languages for rapidly manipulating linear optimal control systems.4 We combine these elements to build a class of models for which the competitive equilibria are vector autoregressions that can be swiftly computed, represented, and simulated using the methods of linear optimal control theory. We use the computer language Matlab to implement the computations. This language has a powerful vocabulary and a convenient structure that liberate time and energy from programming, and thereby spur creative application of linear control theory. Our goal has been to create a class of models that merge recursive economic theory and with dynamic econometrics. Systems of autoregressions and of mixed autogregressive, moving average processes are a dominant setting for dynamic econometrics. We constructed our economic models by adopting a version of recursive competitive theory in which an outcome of theorizing is a vector autoregression.

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